{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Embeddings for Weather Data\n",
    "\n",
    "An embedding is a low-dimensional, vector representation of a (typically) high-dimensional feature which maintains the semantic meaning of the feature in a such a way that similar features are close in the embedding space.\n",
    "\n",
    "In this notebook, we use autoencoders to create embeddings for HRRR images. We can then use the embeddings to search for \"similar\" weather patterns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading package lists...\n",
      "Building dependency tree...\n",
      "Reading state information...\n",
      "libeccodes0 is already the newest version (2.0.2-5).\n",
      "0 upgraded, 0 newly installed, 0 to remove and 1 not upgraded.\n"
     ]
    }
   ],
   "source": [
    "!sudo apt-get -y --quiet install libeccodes0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "%pip install -q cfgrib xarray pydot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.23.0\n"
     ]
    }
   ],
   "source": [
    "import apache_beam as beam\n",
    "print(beam.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "PROJECT='ai-analytics-solutions'\n",
    "BUCKET='{}-kfpdemo'.format(PROJECT)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reading HRRR data and converting to TensorFlow Records\n",
    "\n",
    "HRRR data comes in a Grib2 files on Cloud Storage."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 105624174  2020-08-11T00:51:01Z  gs://high-resolution-rapid-refresh/hrrr.20200811/conus/hrrr.t00z.wrfsfcf00.grib2\n",
      "      7842  2020-08-11T00:50:39Z  gs://high-resolution-rapid-refresh/hrrr.20200811/conus/hrrr.t00z.wrfsfcf00.grib2.idx\n",
      " 104905515  2020-08-11T01:49:31Z  gs://high-resolution-rapid-refresh/hrrr.20200811/conus/hrrr.t01z.wrfsfcf00.grib2\n",
      "      7842  2020-08-11T01:49:04Z  gs://high-resolution-rapid-refresh/hrrr.20200811/conus/hrrr.t01z.wrfsfcf00.grib2.idx\n",
      " 101942347  2020-08-11T02:48:01Z  gs://high-resolution-rapid-refresh/hrrr.20200811/conus/hrrr.t02z.wrfsfcf00.grib2\n",
      "      7840  2020-08-11T02:47:33Z  gs://high-resolution-rapid-refresh/hrrr.20200811/conus/hrrr.t02z.wrfsfcf00.grib2.idx\n",
      " 101412287  2020-08-11T03:52:23Z  gs://high-resolution-rapid-refresh/hrrr.20200811/conus/hrrr.t03z.wrfsfcf00.grib2\n",
      "      7839  2020-08-11T03:51:52Z  gs://high-resolution-rapid-refresh/hrrr.20200811/conus/hrrr.t03z.wrfsfcf00.grib2.idx\n",
      " 101204218  2020-08-11T04:50:36Z  gs://high-resolution-rapid-refresh/hrrr.20200811/conus/hrrr.t04z.wrfsfcf00.grib2\n",
      "      7838  2020-08-11T04:50:07Z  gs://high-resolution-rapid-refresh/hrrr.20200811/conus/hrrr.t04z.wrfsfcf00.grib2.idx\n",
      " 101573630  2020-08-11T05:54:28Z  gs://high-resolution-rapid-refresh/hrrr.20200811/conus/hrrr.t05z.wrfsfcf00.grib2\n",
      "      7839  2020-08-11T05:54:01Z  gs://high-resolution-rapid-refresh/hrrr.20200811/conus/hrrr.t05z.wrfsfcf00.grib2.idx\n",
      " 102636945  2020-08-11T06:53:42Z  gs://high-resolution-rapid-refresh/hrrr.20200811/conus/hrrr.t06z.wrfsfcf00.grib2\n",
      "      7839  2020-08-11T06:47:39Z  gs://high-resolution-rapid-refresh/hrrr.20200811/conus/hrrr.t06z.wrfsfcf00.grib2.idx\n",
      " 101907059  2020-08-11T07:52:20Z  gs://high-resolution-rapid-refresh/hrrr.20200811/conus/hrrr.t07z.wrfsfcf00.grib2\n",
      "      7839  2020-08-11T07:51:50Z  gs://high-resolution-rapid-refresh/hrrr.20200811/conus/hrrr.t07z.wrfsfcf00.grib2.idx\n",
      " 101909124  2020-08-11T08:51:15Z  gs://high-resolution-rapid-refresh/hrrr.20200811/conus/hrrr.t08z.wrfsfcf00.grib2\n",
      "      7839  2020-08-11T08:50:52Z  gs://high-resolution-rapid-refresh/hrrr.20200811/conus/hrrr.t08z.wrfsfcf00.grib2.idx\n",
      " 102567353  2020-08-11T09:56:20Z  gs://high-resolution-rapid-refresh/hrrr.20200811/conus/hrrr.t09z.wrfsfcf00.grib2\n",
      "      7839  2020-08-11T09:50:29Z  gs://high-resolution-rapid-refresh/hrrr.20200811/conus/hrrr.t09z.wrfsfcf00.grib2.idx\n",
      " 100927733  2020-08-11T10:50:18Z  gs://high-resolution-rapid-refresh/hrrr.20200811/conus/hrrr.t10z.wrfsfcf00.grib2\n",
      "      7838  2020-08-11T10:49:52Z  gs://high-resolution-rapid-refresh/hrrr.20200811/conus/hrrr.t10z.wrfsfcf00.grib2.idx\n",
      " 102314449  2020-08-11T11:49:59Z  gs://high-resolution-rapid-refresh/hrrr.20200811/conus/hrrr.t11z.wrfsfcf00.grib2\n",
      "      7839  2020-08-11T11:49:32Z  gs://high-resolution-rapid-refresh/hrrr.20200811/conus/hrrr.t11z.wrfsfcf00.grib2.idx\n",
      " 104575946  2020-08-11T12:50:16Z  gs://high-resolution-rapid-refresh/hrrr.20200811/conus/hrrr.t12z.wrfsfcf00.grib2\n",
      "      7841  2020-08-11T12:49:49Z  gs://high-resolution-rapid-refresh/hrrr.20200811/conus/hrrr.t12z.wrfsfcf00.grib2.idx\n",
      " 106005676  2020-08-11T13:49:59Z  gs://high-resolution-rapid-refresh/hrrr.20200811/conus/hrrr.t13z.wrfsfcf00.grib2\n",
      "      7844  2020-08-11T13:49:29Z  gs://high-resolution-rapid-refresh/hrrr.20200811/conus/hrrr.t13z.wrfsfcf00.grib2.idx\n",
      " 107519378  2020-08-11T14:49:51Z  gs://high-resolution-rapid-refresh/hrrr.20200811/conus/hrrr.t14z.wrfsfcf00.grib2\n",
      "      7845  2020-08-11T14:49:22Z  gs://high-resolution-rapid-refresh/hrrr.20200811/conus/hrrr.t14z.wrfsfcf00.grib2.idx\n",
      " 107689362  2020-08-11T15:49:56Z  gs://high-resolution-rapid-refresh/hrrr.20200811/conus/hrrr.t15z.wrfsfcf00.grib2\n",
      "      7845  2020-08-11T15:54:58Z  gs://high-resolution-rapid-refresh/hrrr.20200811/conus/hrrr.t15z.wrfsfcf00.grib2.idx\n",
      " 105920144  2020-08-11T17:01:23Z  gs://high-resolution-rapid-refresh/hrrr.20200811/conus/hrrr.t16z.wrfsfcf00.grib2\n",
      "      7844  2020-08-11T17:00:54Z  gs://high-resolution-rapid-refresh/hrrr.20200811/conus/hrrr.t16z.wrfsfcf00.grib2.idx\n",
      " 106212316  2020-08-11T17:56:26Z  gs://high-resolution-rapid-refresh/hrrr.20200811/conus/hrrr.t17z.wrfsfcf00.grib2\n",
      "      7844  2020-08-11T17:55:59Z  gs://high-resolution-rapid-refresh/hrrr.20200811/conus/hrrr.t17z.wrfsfcf00.grib2.idx\n",
      " 106833160  2020-08-11T18:51:03Z  gs://high-resolution-rapid-refresh/hrrr.20200811/conus/hrrr.t18z.wrfsfcf00.grib2\n",
      "      7844  2020-08-11T18:50:33Z  gs://high-resolution-rapid-refresh/hrrr.20200811/conus/hrrr.t18z.wrfsfcf00.grib2.idx\n",
      " 109204759  2020-08-11T19:52:08Z  gs://high-resolution-rapid-refresh/hrrr.20200811/conus/hrrr.t19z.wrfsfcf00.grib2\n",
      "      7847  2020-08-11T19:51:38Z  gs://high-resolution-rapid-refresh/hrrr.20200811/conus/hrrr.t19z.wrfsfcf00.grib2.idx\n",
      " 110592176  2020-08-11T20:52:25Z  gs://high-resolution-rapid-refresh/hrrr.20200811/conus/hrrr.t20z.wrfsfcf00.grib2\n",
      "      7847  2020-08-11T20:51:53Z  gs://high-resolution-rapid-refresh/hrrr.20200811/conus/hrrr.t20z.wrfsfcf00.grib2.idx\n",
      " 112848898  2020-08-11T21:52:41Z  gs://high-resolution-rapid-refresh/hrrr.20200811/conus/hrrr.t21z.wrfsfcf00.grib2\n",
      "      7849  2020-08-11T21:52:05Z  gs://high-resolution-rapid-refresh/hrrr.20200811/conus/hrrr.t21z.wrfsfcf00.grib2.idx\n",
      " 111601558  2020-08-11T22:47:42Z  gs://high-resolution-rapid-refresh/hrrr.20200811/conus/hrrr.t22z.wrfsfcf00.grib2\n",
      "      7849  2020-08-11T22:52:37Z  gs://high-resolution-rapid-refresh/hrrr.20200811/conus/hrrr.t22z.wrfsfcf00.grib2.idx\n",
      " 111148344  2020-08-11T23:54:21Z  gs://high-resolution-rapid-refresh/hrrr.20200811/conus/hrrr.t23z.wrfsfcf00.grib2\n",
      "      7849  2020-08-11T23:53:53Z  gs://high-resolution-rapid-refresh/hrrr.20200811/conus/hrrr.t23z.wrfsfcf00.grib2.idx\n",
      "TOTAL: 48 objects, 2529264772 bytes (2.36 GiB)\n"
     ]
    }
   ],
   "source": [
    "!gsutil ls -l gs://high-resolution-rapid-refresh/hrrr.20200811/conus/hrrr.*.wrfsfcf00*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " 128701013  2020-08-11T19:02:22Z  gs://high-resolution-rapid-refresh/hrrr.20200811/conus/hrrr.t18z.wrfsfcf06.grib2\n",
      "TOTAL: 1 objects, 128701013 bytes (122.74 MiB)\n"
     ]
    }
   ],
   "source": [
    "FILENAME=\"gs://high-resolution-rapid-refresh/hrrr.20200811/conus/hrrr.t18z.wrfsfcf06.grib2\"   # derecho in the Midwest\n",
    "!gsutil ls -l {FILENAME}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import xarray as xr\n",
    "import tensorflow as tf\n",
    "import tempfile\n",
    "import cfgrib\n",
    "\n",
    "with tempfile.TemporaryDirectory() as tmpdirname:\n",
    "    TMPFILE=\"{}/read_grib\".format(tmpdirname)\n",
    "    tf.io.gfile.copy(FILENAME, TMPFILE, overwrite=True)\n",
    "    ds = cfgrib.open_datasets(TMPFILE)\n",
    "    print(ds)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have to choose one of the following:\n",
    "```\n",
    "    filter_by_keys={'typeOfLevel': 'unknown'}\n",
    "    filter_by_keys={'typeOfLevel': 'cloudTop'}\n",
    "    filter_by_keys={'typeOfLevel': 'surface'}\n",
    "    filter_by_keys={'typeOfLevel': 'heightAboveGround'}\n",
    "    filter_by_keys={'typeOfLevel': 'isothermal'}\n",
    "    filter_by_keys={'typeOfLevel': 'isobaricInhPa'}\n",
    "    filter_by_keys={'typeOfLevel': 'pressureFromGroundLayer'}\n",
    "    filter_by_keys={'typeOfLevel': 'sigmaLayer'}\n",
    "    filter_by_keys={'typeOfLevel': 'meanSea'}\n",
    "    filter_by_keys={'typeOfLevel': 'heightAboveGroundLayer'}\n",
    "    filter_by_keys={'typeOfLevel': 'sigma'}\n",
    "    filter_by_keys={'typeOfLevel': 'depthBelowLand'}\n",
    "    filter_by_keys={'typeOfLevel': 'isobaricLayer'}\n",
    "    filter_by_keys={'typeOfLevel': 'cloudBase'}\n",
    "    filter_by_keys={'typeOfLevel': 'nominalTop'}\n",
    "    filter_by_keys={'typeOfLevel': 'isothermZero'}\n",
    "    filter_by_keys={'typeOfLevel': 'adiabaticCondensation'}\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1059 1799]\n",
      "2020-08-11T18:00:00.000000000\n",
      "2020-08-12T00:00:00.000000000\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import xarray as xr\n",
    "import tensorflow as tf\n",
    "import tempfile\n",
    "import cfgrib\n",
    "import numpy as np\n",
    "\n",
    "refc = 0\n",
    "with tempfile.TemporaryDirectory() as tmpdirname:\n",
    "    TMPFILE=\"{}/read_grib\".format(tmpdirname)\n",
    "    tf.io.gfile.copy(FILENAME, TMPFILE, overwrite=True)\n",
    "    #ds = xr.open_dataset(TMPFILE, engine='cfgrib', backend_kwargs={'filter_by_keys': {'typeOfLevel': 'surface', 'stepType': 'instant'}})\n",
    "    #ds.data_vars['prate'].plot()  # crain, prate\n",
    "    ds = xr.open_dataset(TMPFILE, engine='cfgrib', backend_kwargs={'filter_by_keys': {'typeOfLevel': 'unknown', 'stepType': 'instant'}})\n",
    "    #ds = xr.open_dataset(TMPFILE, engine='cfgrib', backend_kwargs={'filter_by_keys': {'typeOfLevel': 'atmosphere', 'stepType': 'instant'}})\n",
    "    refc = ds.data_vars['refc']\n",
    "    refc.plot()\n",
    "    print(np.array([refc.sizes['y'], refc.sizes['x']]))\n",
    "    print(refc.time.data)\n",
    "    print(refc.valid_time.data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2020-08-11T18:00:00\n"
     ]
    }
   ],
   "source": [
    "print(str(refc.time.data)[:19])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7620594 b'\\n\\xed\\x8f\\xd1\\x03\\n\\xe8\\x8f\\xd1\\x03\\n\\x03ref\\x12'\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "def _array_feature(value, min_value, max_value):\n",
    "    \"\"\"Wrapper for inserting ndarray float features into Example proto.\"\"\"\n",
    "    value = np.nan_to_num(value.flatten()) # nan, -inf, +inf to numbers\n",
    "    value = np.clip(value, min_value, max_value) # clip to valid\n",
    "    return tf.train.Feature(float_list=tf.train.FloatList(value=value))\n",
    "\n",
    "def create_tfrecord(filename):\n",
    "    with tempfile.TemporaryDirectory() as tmpdirname:\n",
    "        TMPFILE=\"{}/read_grib\".format(tmpdirname)\n",
    "        tf.io.gfile.copy(filename, TMPFILE, overwrite=True)\n",
    "        ds = xr.open_dataset(TMPFILE, engine='cfgrib', backend_kwargs={'filter_by_keys': {'typeOfLevel': 'unknown', 'stepType': 'instant'}})\n",
    "   \n",
    "        # create a TF Record with the raw data\n",
    "        tfexample = tf.train.Example(\n",
    "            features=tf.train.Features(\n",
    "                feature={\n",
    "                    'ref': _array_feature(ds.data_vars['refc'].data, min_value=0, max_value=60),\n",
    "        }))\n",
    "        return tfexample.SerializeToString()\n",
    "\n",
    "s = create_tfrecord(FILENAME)\n",
    "print(len(s), s[:16])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['gs://high-resolution-rapid-refresh/hrrr.20190916/conus/hrrr.t09z.wrfsfcf00.grib2', 'gs://high-resolution-rapid-refresh/hrrr.20190915/conus/hrrr.t20z.wrfsfcf00.grib2', 'gs://high-resolution-rapid-refresh/hrrr.20190916/conus/hrrr.t00z.wrfsfcf00.grib2', 'gs://high-resolution-rapid-refresh/hrrr.20190916/conus/hrrr.t06z.wrfsfcf00.grib2', 'gs://high-resolution-rapid-refresh/hrrr.20190916/conus/hrrr.t17z.wrfsfcf00.grib2', 'gs://high-resolution-rapid-refresh/hrrr.20190916/conus/hrrr.t15z.wrfsfcf00.grib2', 'gs://high-resolution-rapid-refresh/hrrr.20190915/conus/hrrr.t22z.wrfsfcf00.grib2', 'gs://high-resolution-rapid-refresh/hrrr.20190916/conus/hrrr.t01z.wrfsfcf00.grib2', 'gs://high-resolution-rapid-refresh/hrrr.20190916/conus/hrrr.t03z.wrfsfcf00.grib2', 'gs://high-resolution-rapid-refresh/hrrr.20190915/conus/hrrr.t21z.wrfsfcf00.grib2', 'gs://high-resolution-rapid-refresh/hrrr.20190916/conus/hrrr.t02z.wrfsfcf00.grib2', 'gs://high-resolution-rapid-refresh/hrrr.20190916/conus/hrrr.t20z.wrfsfcf00.grib2', 'gs://high-resolution-rapid-refresh/hrrr.20190916/conus/hrrr.t18z.wrfsfcf00.grib2', 'gs://high-resolution-rapid-refresh/hrrr.20190916/conus/hrrr.t05z.wrfsfcf00.grib2', 'gs://high-resolution-rapid-refresh/hrrr.20190916/conus/hrrr.t08z.wrfsfcf00.grib2', 'gs://high-resolution-rapid-refresh/hrrr.20190916/conus/hrrr.t23z.wrfsfcf00.grib2', 'gs://high-resolution-rapid-refresh/hrrr.20190917/conus/hrrr.t00z.wrfsfcf00.grib2', 'gs://high-resolution-rapid-refresh/hrrr.20190915/conus/hrrr.t03z.wrfsfcf00.grib2', 'gs://high-resolution-rapid-refresh/hrrr.20190915/conus/hrrr.t09z.wrfsfcf00.grib2', 'gs://high-resolution-rapid-refresh/hrrr.20190916/conus/hrrr.t13z.wrfsfcf00.grib2', 'gs://high-resolution-rapid-refresh/hrrr.20190915/conus/hrrr.t12z.wrfsfcf00.grib2', 'gs://high-resolution-rapid-refresh/hrrr.20190915/conus/hrrr.t11z.wrfsfcf00.grib2', 'gs://high-resolution-rapid-refresh/hrrr.20190915/conus/hrrr.t01z.wrfsfcf00.grib2', 'gs://high-resolution-rapid-refresh/hrrr.20190916/conus/hrrr.t11z.wrfsfcf00.grib2', 'gs://high-resolution-rapid-refresh/hrrr.20190916/conus/hrrr.t04z.wrfsfcf00.grib2', 'gs://high-resolution-rapid-refresh/hrrr.20190916/conus/hrrr.t21z.wrfsfcf00.grib2', 'gs://high-resolution-rapid-refresh/hrrr.20190916/conus/hrrr.t16z.wrfsfcf00.grib2', 'gs://high-resolution-rapid-refresh/hrrr.20190915/conus/hrrr.t23z.wrfsfcf00.grib2', 'gs://high-resolution-rapid-refresh/hrrr.20190915/conus/hrrr.t06z.wrfsfcf00.grib2', 'gs://high-resolution-rapid-refresh/hrrr.20190915/conus/hrrr.t00z.wrfsfcf00.grib2', 'gs://high-resolution-rapid-refresh/hrrr.20190915/conus/hrrr.t15z.wrfsfcf00.grib2', 'gs://high-resolution-rapid-refresh/hrrr.20190915/conus/hrrr.t08z.wrfsfcf00.grib2', 'gs://high-resolution-rapid-refresh/hrrr.20190916/conus/hrrr.t12z.wrfsfcf00.grib2', 'gs://high-resolution-rapid-refresh/hrrr.20190915/conus/hrrr.t10z.wrfsfcf00.grib2', 'gs://high-resolution-rapid-refresh/hrrr.20190916/conus/hrrr.t10z.wrfsfcf00.grib2', 'gs://high-resolution-rapid-refresh/hrrr.20190916/conus/hrrr.t07z.wrfsfcf00.grib2', 'gs://high-resolution-rapid-refresh/hrrr.20190915/conus/hrrr.t18z.wrfsfcf00.grib2', 'gs://high-resolution-rapid-refresh/hrrr.20190916/conus/hrrr.t19z.wrfsfcf00.grib2', 'gs://high-resolution-rapid-refresh/hrrr.20190916/conus/hrrr.t22z.wrfsfcf00.grib2', 'gs://high-resolution-rapid-refresh/hrrr.20190915/conus/hrrr.t14z.wrfsfcf00.grib2', 'gs://high-resolution-rapid-refresh/hrrr.20190916/conus/hrrr.t14z.wrfsfcf00.grib2', 'gs://high-resolution-rapid-refresh/hrrr.20190915/conus/hrrr.t13z.wrfsfcf00.grib2', 'gs://high-resolution-rapid-refresh/hrrr.20190915/conus/hrrr.t02z.wrfsfcf00.grib2', 'gs://high-resolution-rapid-refresh/hrrr.20190915/conus/hrrr.t16z.wrfsfcf00.grib2', 'gs://high-resolution-rapid-refresh/hrrr.20190915/conus/hrrr.t17z.wrfsfcf00.grib2', 'gs://high-resolution-rapid-refresh/hrrr.20190915/conus/hrrr.t19z.wrfsfcf00.grib2', 'gs://high-resolution-rapid-refresh/hrrr.20190915/conus/hrrr.t07z.wrfsfcf00.grib2', 'gs://high-resolution-rapid-refresh/hrrr.20190915/conus/hrrr.t05z.wrfsfcf00.grib2', 'gs://high-resolution-rapid-refresh/hrrr.20190915/conus/hrrr.t04z.wrfsfcf00.grib2']\n"
     ]
    }
   ],
   "source": [
    "from datetime import datetime, timedelta\n",
    "def generate_filenames(startdate: str, enddate: str):\n",
    "    start_dt = datetime.strptime(startdate, '%Y%m%d')\n",
    "    end_dt = datetime.strptime(enddate, '%Y%m%d')\n",
    "    dt = start_dt\n",
    "    while dt <= end_dt:\n",
    "        # gs://high-resolution-rapid-refresh/hrrr.20200811/conus/hrrr.t04z.wrfsfcf00.grib2\n",
    "        f = '{}/hrrr.{:4}{:02}{:02}/conus/hrrr.t{:02}z.wrfsfcf00.grib2'.format(\n",
    "                'gs://high-resolution-rapid-refresh',\n",
    "                dt.year, dt.month, dt.day, dt.hour)\n",
    "        dt = dt + timedelta(hours=1)\n",
    "        yield f\n",
    "        \n",
    "def generate_shuffled_filenames(startdate: str, enddate: str):\n",
    "    \"\"\"\n",
    "    shuffle the files so that a batch of records doesn't contain highly correlated entries\n",
    "    \"\"\"\n",
    "    filenames = [f for f in generate_filenames(startdate, enddate)]\n",
    "    np.random.shuffle(filenames)\n",
    "    return filenames\n",
    "\n",
    "print(generate_shuffled_filenames('20190915', '20190917'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Write a Beam pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%run -m wxsearch.hrrr_to_tfrecord -- --startdate 20190915 --enddate 20190916  --outdir gs://{BUCKET}/wxsearch/data/2019 --project {PROJECT}\n",
    "# --outdir tmp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read the written TF Records"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'ref': <tf.Tensor: shape=(1059, 1799), dtype=float32, numpy=\n",
      "array([[0., 0., 0., ..., 0., 0., 0.],\n",
      "       [0., 0., 0., ..., 0., 0., 0.],\n",
      "       [0., 0., 0., ..., 0., 0., 0.],\n",
      "       ...,\n",
      "       [0., 0., 0., ..., 0., 0., 0.],\n",
      "       [0., 0., 0., ..., 0., 0., 0.],\n",
      "       [0., 0., 0., ..., 0., 0., 0.]], dtype=float32)>, 'size': <tf.Tensor: shape=(2,), dtype=int64, numpy=array([1059, 1799])>, 'time': <tf.Tensor: shape=(), dtype=string, numpy=b'2019-06-06T03:00:00'>, 'valid_time': <tf.Tensor: shape=(), dtype=string, numpy=b'2019-06-06T03:00:00'>}\n"
     ]
    }
   ],
   "source": [
    "# try reading what was written out\n",
    "import tensorflow as tf\n",
    "\n",
    "def parse_tfrecord(example_data):\n",
    "    parsed = tf.io.parse_single_example(example_data, {\n",
    "        'size': tf.io.VarLenFeature(tf.int64),\n",
    "        'ref': tf.io.VarLenFeature(tf.float32),\n",
    "        'time': tf.io.FixedLenFeature([], tf.string),\n",
    "        'valid_time': tf.io.FixedLenFeature([], tf.string)\n",
    "     })\n",
    "    parsed['size'] = tf.sparse.to_dense(parsed['size'])\n",
    "    parsed['ref'] = tf.reshape(tf.sparse.to_dense(parsed['ref']), (1059, 1799))/60. # 0 to 1\n",
    "    return parsed\n",
    "\n",
    "def read_dataset(pattern):\n",
    "    filenames = tf.io.gfile.glob(pattern)\n",
    "    ds = tf.data.TFRecordDataset(filenames, compression_type=None, buffer_size=None, num_parallel_reads=None)\n",
    "    return ds.prefetch(tf.data.experimental.AUTOTUNE).map(parse_tfrecord)\n",
    "\n",
    "ds = read_dataset('gs://{}/wxsearch/data/2019/tfrecord-00000-*'.format(BUCKET))\n",
    "for refc in ds.take(1):\n",
    "    print(repr(refc))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create autoencoder in Keras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "50 969123\n",
      "Model: \"autoencoder\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "refc_input (InputLayer)      [(None, 1059, 1799, 1)]   0         \n",
      "_________________________________________________________________\n",
      "cropped (Cropping2D)         (None, 1024, 1792, 1)     0         \n",
      "_________________________________________________________________\n",
      "encoder_conv_0 (Conv2D)      (None, 1024, 1792, 16)    272       \n",
      "_________________________________________________________________\n",
      "encoder_pool_0 (MaxPooling2D (None, 256, 448, 16)      0         \n",
      "_________________________________________________________________\n",
      "encoder_conv_1 (Conv2D)      (None, 256, 448, 32)      8224      \n",
      "_________________________________________________________________\n",
      "encoder_pool_1 (MaxPooling2D (None, 64, 112, 32)       0         \n",
      "_________________________________________________________________\n",
      "encoder_conv_2 (Conv2D)      (None, 64, 112, 64)       32832     \n",
      "_________________________________________________________________\n",
      "encoder_pool_2 (MaxPooling2D (None, 16, 28, 64)        0         \n",
      "_________________________________________________________________\n",
      "encoder_conv_3 (Conv2D)      (None, 16, 28, 128)       131200    \n",
      "_________________________________________________________________\n",
      "encoder_pool_3 (MaxPooling2D (None, 4, 7, 128)         0         \n",
      "_________________________________________________________________\n",
      "encoder_flatten (Flatten)    (None, 3584)              0         \n",
      "_________________________________________________________________\n",
      "refc_embedding (Dense)       (None, 50)                179250    \n",
      "_________________________________________________________________\n",
      "decoder_dense (Dense)        (None, 3584)              182784    \n",
      "_________________________________________________________________\n",
      "decoder_reshape (Reshape)    (None, 4, 7, 128)         0         \n",
      "_________________________________________________________________\n",
      "decoder_conv_0 (Conv2D)      (None, 4, 7, 128)         262272    \n",
      "_________________________________________________________________\n",
      "decoder_upsamp_0 (UpSampling (None, 16, 28, 128)       0         \n",
      "_________________________________________________________________\n",
      "decoder_conv_1 (Conv2D)      (None, 16, 28, 64)        131136    \n",
      "_________________________________________________________________\n",
      "decoder_upsamp_1 (UpSampling (None, 64, 112, 64)       0         \n",
      "_________________________________________________________________\n",
      "decoder_conv_2 (Conv2D)      (None, 64, 112, 32)       32800     \n",
      "_________________________________________________________________\n",
      "decoder_upsamp_2 (UpSampling (None, 256, 448, 32)      0         \n",
      "_________________________________________________________________\n",
      "decoder_conv_3 (Conv2D)      (None, 256, 448, 16)      8208      \n",
      "_________________________________________________________________\n",
      "decoder_upsamp_3 (UpSampling (None, 1024, 1792, 16)    0         \n",
      "_________________________________________________________________\n",
      "before_padding (Conv2D)      (None, 1024, 1792, 1)     145       \n",
      "_________________________________________________________________\n",
      "refc_reconstructed (ZeroPadd (None, 1059, 1799, 1)     0         \n",
      "=================================================================\n",
      "Total params: 969,123\n",
      "Trainable params: 969,123\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "## A model without the intermediate Dense layer, so that end result is effectively tiled\n",
    "## We use more filters to represent the tiles\n",
    "\n",
    "import tensorflow as tf\n",
    "def create_model(nlayers=4, poolsize=4, numfilters=5, num_dense=0):\n",
    "    input_img = tf.keras.Input(shape=(1059, 1799, 1), name='refc_input')\n",
    "\n",
    "    x = tf.keras.layers.Cropping2D(cropping=((17, 18),(4, 3)), name='cropped')(input_img)\n",
    "    last_pool_layer = None\n",
    "    for layerno in range(nlayers):\n",
    "        x = tf.keras.layers.Conv2D(2**(layerno + numfilters), poolsize, activation='relu', padding='same', name='encoder_conv_{}'.format(layerno))(x)\n",
    "        last_pool_layer = tf.keras.layers.MaxPooling2D(poolsize, padding='same', name='encoder_pool_{}'.format(layerno))\n",
    "        x = last_pool_layer(x)\n",
    "    output_shape = last_pool_layer.output_shape[1:]\n",
    "    \n",
    "    if num_dense == 0:\n",
    "        # flatten to create the embedding\n",
    "        x = tf.keras.layers.Flatten(name='refc_embedding')(x)\n",
    "        embed_size = output_shape[0] * output_shape[1] * output_shape[2]\n",
    "        if embed_size > 1024:\n",
    "            print(\"Embedding size={} is too large\".format(embed_size))\n",
    "            return None, embed_size\n",
    "    else:\n",
    "        # flatten, send through dense layer to create the embedding\n",
    "        x = tf.keras.layers.Flatten(name='encoder_flatten')(x)\n",
    "        x = tf.keras.layers.Dense(num_dense, name='refc_embedding')(x)\n",
    "        x = tf.keras.layers.Dense(output_shape[0] * output_shape[1] * output_shape[2], name='decoder_dense')(x)\n",
    "        embed_size = num_dense\n",
    "        \n",
    "    x = tf.keras.layers.Reshape(output_shape, name='decoder_reshape')(x)\n",
    "    for layerno in range(nlayers):\n",
    "        x = tf.keras.layers.Conv2D(2**(nlayers-layerno-1 + numfilters), poolsize, activation='relu', padding='same', name='decoder_conv_{}'.format(layerno))(x)\n",
    "        x = tf.keras.layers.UpSampling2D(poolsize, name='decoder_upsamp_{}'.format(layerno))(x)\n",
    "    before_padding_layer = tf.keras.layers.Conv2D(1, 3, activation='relu', padding='same', name='before_padding')\n",
    "    x = before_padding_layer(x)\n",
    "    htdiff = 1059 - before_padding_layer.output_shape[1]\n",
    "    wddiff = 1799 - before_padding_layer.output_shape[2]\n",
    "    if htdiff < 0 or wddiff < 0:\n",
    "        print(\"Invalid architecture: htdiff={} wddiff={}\".format(htdiff, wddiff))\n",
    "        return None, 9999\n",
    "    decoded = tf.keras.layers.ZeroPadding2D(padding=((htdiff//2,htdiff - htdiff//2),\n",
    "                                                     (wddiff//2,wddiff - wddiff//2)), name='refc_reconstructed')(x)\n",
    "\n",
    "    autoencoder = tf.keras.Model(input_img, decoded, name='autoencoder')\n",
    "    autoencoder.compile(optimizer='adam', loss=tf.keras.losses.LogCosh()) #loss='mse')\n",
    "    if autoencoder.count_params() > 1000*1000: # 1 million      \n",
    "        print(\"Autoencoder too large: {} params\".format(autoencoder.count_params()))\n",
    "        return None, autoencoder.count_params()\n",
    "    \n",
    "    return autoencoder, embed_size\n",
    "\n",
    "autoencoder, sz = create_model(4, 4, 4, 50)\n",
    "if autoencoder:\n",
    "    print(sz, autoencoder.count_params())\n",
    "    autoencoder.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train the autoencoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def input_and_label(rec):\n",
    "    return rec['ref'], rec['ref']\n",
    "\n",
    "ds = read_dataset('gs://{}/wxsearch/data/2019/tfrecord-00000-*'.format(BUCKET)).map(input_and_label).batch(2).repeat()\n",
    "checkpoint = tf.keras.callbacks.ModelCheckpoint('tmp/checkpoints')\n",
    "history = autoencoder.fit(ds, steps_per_epoch=1, epochs=3, shuffle=True, callbacks=[checkpoint])\n",
    "print(history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "autoencoder.save('tmp/savedmodel')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "plt.plot(history.history['loss']);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%run -m wxsearch.train_autoencoder -- --input gs://{BUCKET}/wxsearch/data/2019/tfrecord-00000-*  --outdir gs://{BUCKET}/wxsearch/trained --project {PROJECT}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Run at scale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%run -m wxsearch.hrrr_to_tfrecord -- --startdate 20190101 --enddate 20200101  --outdir gs://{BUCKET}/wxsearch/data/2019 --project {PROJECT}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"dataflow_2019.png\" />"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overwriting train.yaml\n"
     ]
    }
   ],
   "source": [
    "%%writefile train.yaml\n",
    "trainingInput:\n",
    "  scaleTier: CUSTOM\n",
    "  masterType: n1-highmem-2\n",
    "  masterConfig:\n",
    "    acceleratorConfig:\n",
    "      count: 2\n",
    "      type: NVIDIA_TESLA_K80\n",
    "  runtimeVersion: '2.2'\n",
    "  pythonVersion: '3.7'\n",
    "  scheduling:\n",
    "    maxWaitTime: 3600s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ai-analytics-solutions\n",
      "jobId: wxsearch_20201009_010505\n",
      "state: QUEUED\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Job [wxsearch_20201009_010505] submitted successfully.\n",
      "Your job is still active. You may view the status of your job with the command\n",
      "\n",
      "  $ gcloud ai-platform jobs describe wxsearch_20201009_010505\n",
      "\n",
      "or continue streaming the logs with the command\n",
      "\n",
      "  $ gcloud ai-platform jobs stream-logs wxsearch_20201009_010505\n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "\n",
    "PROJECT=$(gcloud config get-value project)\n",
    "echo ${PROJECT}\n",
    "BUCKET=\"ai-analytics-solutions-kfpdemo\"\n",
    "PACKAGE_PATH=\"${PWD}/wxsearch\"\n",
    "now=$(date +\"%Y%m%d_%H%M%S\")\n",
    "JOB_NAME=\"wxsearch_$now\"\n",
    "MODULE_NAME=\"wxsearch.train_autoencoder\"\n",
    "JOB_DIR=\"gs://${BUCKET}/wxsearch/train/jobdir\"\n",
    "REGION=\"us-central1\"\n",
    "\n",
    "# 9000 images in dataset\n",
    "gcloud ai-platform jobs submit training $JOB_NAME \\\n",
    "        --package-path $PACKAGE_PATH \\\n",
    "        --module-name $MODULE_NAME \\\n",
    "        --job-dir $JOB_DIR \\\n",
    "        --region $REGION \\\n",
    "        --config train.yaml \\\n",
    "        -- \\\n",
    "        --input gs://${BUCKET}/wxsearch/data/2019/tfrecord-* \\\n",
    "        --outdir gs://${BUCKET}/wxsearch/trained \\\n",
    "        --project ${PROJECT} \\\n",
    "        --batch_size 4 --num_steps 10000 --num_checkpoints 10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Try out the autoencoder\n",
    "\n",
    "Load the Keras model, and try out the autoencoder functionality."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "model = tf.keras.models.load_model('gs://ai-analytics-solutions-kfpdemo/wxsearch/trained/savedmodel')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"embedder\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "refc_input (InputLayer)      [(None, 1059, 1799, 1)]   0         \n",
      "_________________________________________________________________\n",
      "cropped (Cropping2D)         (None, 1024, 1792, 1)     0         \n",
      "_________________________________________________________________\n",
      "encoder_conv_0 (Conv2D)      (None, 1024, 1792, 16)    272       \n",
      "_________________________________________________________________\n",
      "encoder_pool_0 (MaxPooling2D (None, 256, 448, 16)      0         \n",
      "_________________________________________________________________\n",
      "encoder_conv_1 (Conv2D)      (None, 256, 448, 32)      8224      \n",
      "_________________________________________________________________\n",
      "encoder_pool_1 (MaxPooling2D (None, 64, 112, 32)       0         \n",
      "_________________________________________________________________\n",
      "encoder_conv_2 (Conv2D)      (None, 64, 112, 64)       32832     \n",
      "_________________________________________________________________\n",
      "encoder_pool_2 (MaxPooling2D (None, 16, 28, 64)        0         \n",
      "_________________________________________________________________\n",
      "encoder_conv_3 (Conv2D)      (None, 16, 28, 128)       131200    \n",
      "_________________________________________________________________\n",
      "encoder_pool_3 (MaxPooling2D (None, 4, 7, 128)         0         \n",
      "_________________________________________________________________\n",
      "encoder_flatten (Flatten)    (None, 3584)              0         \n",
      "_________________________________________________________________\n",
      "refc_embedding (Dense)       (None, 50)                179250    \n",
      "=================================================================\n",
      "Total params: 351,778\n",
      "Trainable params: 351,778\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "embed_output = model.get_layer('refc_embedding').output\n",
    "embedder = tf.keras.Model(model.input, embed_output, name='embedder')\n",
    "print(embedder.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ai-analytics-solutions-kfpdemo\n",
      "tf.Tensor(\n",
      "[[0. 0. 0. ... 0. 0. 0.]\n",
      " [0. 0. 0. ... 0. 0. 0.]\n",
      " [0. 0. 0. ... 0. 0. 0.]\n",
      " ...\n",
      " [0. 0. 0. ... 0. 0. 0.]\n",
      " [0. 0. 0. ... 0. 0. 0.]\n",
      " [0. 0. 0. ... 0. 0. 0.]], shape=(1059, 1799), dtype=float32)\n",
      "tf.Tensor(\n",
      "[ 0.301531    1.1230749  -0.11051312 -0.7169816   0.41527277 -0.2514371\n",
      "  0.45809814 -0.1219312   0.6078904  -0.24121551  0.51386464 -0.15141343\n",
      " -0.18161477  0.5419359  -0.02541194  0.1649076   0.43065113  0.691609\n",
      " -0.23777175 -0.2843141  -0.63672084  0.65880483  0.24391702 -0.07345533\n",
      " -0.08864652 -0.14317618  0.38981095 -0.4006542   0.21685484  0.90802234\n",
      " -0.12074046  0.14775434 -0.02762385 -0.47858676 -0.09548081 -0.3523487\n",
      "  0.38083994 -0.19541779 -0.19363467  0.07926624 -0.2369798   0.24522418\n",
      "  1.0302618  -0.0429398  -0.3022312   0.48221755 -0.2893774   0.20048837\n",
      "  0.04749289 -0.00721024], shape=(50,), dtype=float32)\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "PROJECT='ai-analytics-solutions'\n",
    "BUCKET='{}-kfpdemo'.format(PROJECT)\n",
    "print(BUCKET)\n",
    "\n",
    "def parse_tfrecord(example_data):\n",
    "    parsed = tf.io.parse_single_example(example_data, {\n",
    "        'size': tf.io.VarLenFeature(tf.int64),\n",
    "        'ref': tf.io.VarLenFeature(tf.float32),\n",
    "        'time': tf.io.FixedLenFeature([], tf.string),\n",
    "        'valid_time': tf.io.FixedLenFeature([], tf.string)\n",
    "     })\n",
    "    parsed['size'] = tf.sparse.to_dense(parsed['size'])\n",
    "    parsed['ref'] = tf.reshape(tf.sparse.to_dense(parsed['ref']), (1059, 1799))/60. # 0 to 1\n",
    "    return parsed\n",
    "\n",
    "def read_dataset(pattern):\n",
    "    filenames = tf.io.gfile.glob(pattern)\n",
    "    ds = tf.data.TFRecordDataset(filenames, compression_type=None, buffer_size=None, num_parallel_reads=None)\n",
    "    return ds.prefetch(tf.data.experimental.AUTOTUNE).map(parse_tfrecord)\n",
    "\n",
    "ds = read_dataset('gs://{}/wxsearch/data/2019/tfrecord-00000-*'.format(BUCKET))\n",
    "for rec in ds.take(1):\n",
    "    print(rec['ref'])\n",
    "    refc = tf.expand_dims(tf.expand_dims(rec['ref'], 0), -1)\n",
    "    x = embedder.predict(refc)\n",
    "    print(tf.squeeze(x, axis=0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.3.0\n"
     ]
    }
   ],
   "source": [
    "print(tf.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CommandException: One or more URLs matched no objects.\n"
     ]
    }
   ],
   "source": [
    "!gsutil ls gs://ai-analytics-solutions-kfpdemo/wxsearch/data/2019/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%run -m wxsearch.compute_embedding -- --output_table {PROJECT}:advdata.wxembed --savedmodel gs://{BUCKET}/wxsearch/trained/savedmodel --input gs://{BUCKET}/wxsearch/data/2019/tfrecord-* --outdir gs://{BUCKET}/wxsearch/tmp --project {PROJECT}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Try decoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%bigquery df\n",
    "SELECT *\n",
    "FROM advdata.wxembed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>size</th>\n",
       "      <th>ref</th>\n",
       "      <th>time</th>\n",
       "      <th>valid_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[1059, 1799]</td>\n",
       "      <td>[-0.3423147201538086, -0.2379823625087738, 0.1...</td>\n",
       "      <td>2019-09-20 05:00:00+00:00</td>\n",
       "      <td>2019-09-20 05:00:00+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[1059, 1799]</td>\n",
       "      <td>[-0.11114707589149475, 0.2925637364387512, -0....</td>\n",
       "      <td>2019-08-16 10:00:00+00:00</td>\n",
       "      <td>2019-08-16 10:00:00+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[1059, 1799]</td>\n",
       "      <td>[0.37308964133262634, 0.1036672368645668, -0.1...</td>\n",
       "      <td>2019-10-28 23:00:00+00:00</td>\n",
       "      <td>2019-10-28 23:00:00+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[1059, 1799]</td>\n",
       "      <td>[0.3508044183254242, 0.20376038551330566, -0.0...</td>\n",
       "      <td>2019-07-20 11:00:00+00:00</td>\n",
       "      <td>2019-07-20 11:00:00+00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[1059, 1799]</td>\n",
       "      <td>[-0.08892631530761719, 0.10215485841035843, -0...</td>\n",
       "      <td>2019-08-08 20:00:00+00:00</td>\n",
       "      <td>2019-08-08 20:00:00+00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           size                                                ref  \\\n",
       "0  [1059, 1799]  [-0.3423147201538086, -0.2379823625087738, 0.1...   \n",
       "1  [1059, 1799]  [-0.11114707589149475, 0.2925637364387512, -0....   \n",
       "2  [1059, 1799]  [0.37308964133262634, 0.1036672368645668, -0.1...   \n",
       "3  [1059, 1799]  [0.3508044183254242, 0.20376038551330566, -0.0...   \n",
       "4  [1059, 1799]  [-0.08892631530761719, 0.10215485841035843, -0...   \n",
       "\n",
       "                       time                valid_time  \n",
       "0 2019-09-20 05:00:00+00:00 2019-09-20 05:00:00+00:00  \n",
       "1 2019-08-16 10:00:00+00:00 2019-08-16 10:00:00+00:00  \n",
       "2 2019-10-28 23:00:00+00:00 2019-10-28 23:00:00+00:00  \n",
       "3 2019-07-20 11:00:00+00:00 2019-07-20 11:00:00+00:00  \n",
       "4 2019-08-08 20:00:00+00:00 2019-08-08 20:00:00+00:00  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(n=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's decode the first row. First, we create a decoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"decoder\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "embed_input (InputLayer)     [(None, 50)]              0         \n",
      "_________________________________________________________________\n",
      "decoder_dense (Dense)        (None, 3584)              182784    \n",
      "_________________________________________________________________\n",
      "decoder_reshape (Reshape)    (None, 4, 7, 128)         0         \n",
      "_________________________________________________________________\n",
      "decoder_conv_0 (Conv2D)      (None, 4, 7, 128)         262272    \n",
      "_________________________________________________________________\n",
      "decoder_upsamp_0 (UpSampling (None, 16, 28, 128)       0         \n",
      "_________________________________________________________________\n",
      "decoder_conv_1 (Conv2D)      (None, 16, 28, 64)        131136    \n",
      "_________________________________________________________________\n",
      "decoder_upsamp_1 (UpSampling (None, 64, 112, 64)       0         \n",
      "_________________________________________________________________\n",
      "decoder_conv_2 (Conv2D)      (None, 64, 112, 32)       32800     \n",
      "_________________________________________________________________\n",
      "decoder_upsamp_2 (UpSampling (None, 256, 448, 32)      0         \n",
      "_________________________________________________________________\n",
      "decoder_conv_3 (Conv2D)      (None, 256, 448, 16)      8208      \n",
      "_________________________________________________________________\n",
      "decoder_upsamp_3 (UpSampling (None, 1024, 1792, 16)    0         \n",
      "_________________________________________________________________\n",
      "before_padding (Conv2D)      (None, 1024, 1792, 1)     145       \n",
      "_________________________________________________________________\n",
      "refc_reconstructed (ZeroPadd (None, 1059, 1799, 1)     0         \n",
      "=================================================================\n",
      "Total params: 617,345\n",
      "Trainable params: 617,345\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "def create_decoder(model_dir):\n",
    "    model = tf.keras.models.load_model(model_dir)\n",
    "    decoder_input = tf.keras.Input([50], name='embed_input')\n",
    "    embed_seen = False\n",
    "    x = decoder_input\n",
    "    for layer in model.layers:\n",
    "        if embed_seen:\n",
    "            x = layer(x)\n",
    "        elif layer.name == 'refc_embedding':\n",
    "            embed_seen = True\n",
    "    decoder = tf.keras.Model(decoder_input, x, name='decoder')\n",
    "    print(decoder.summary())\n",
    "    return decoder\n",
    "\n",
    "decoder = create_decoder('gs://ai-analytics-solutions-kfpdemo/wxsearch/trained/savedmodel')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then, we invoke decoder.predict() to reconstruct the image from the 50 numbers in the embedding."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "50\n",
      "31.765097\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "embed = tf.reshape( tf.convert_to_tensor(df['ref'].values[0], dtype=tf.float32), [-1, 50])\n",
    "outimg = decoder.predict(embed).squeeze() * 60\n",
    "print(len(df['ref'].values[0]))\n",
    "print(np.max(outimg))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.imshow(outimg, origin='lower');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What does the original look like? Let's pull the original HRRR Grib file from this time stamp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gs://high-resolution-rapid-refresh/hrrr.20190920/conus/hrrr.t05z.wrfsfcf00.grib2\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "dt = pd.Timestamp(df['time'].values[0])\n",
    "# gs://high-resolution-rapid-refresh/hrrr.20200811/conus/hrrr.t04z.wrfsfcf00.grib2\n",
    "FILENAME = '{}/hrrr.{:4}{:02}{:02}/conus/hrrr.t{:02}z.wrfsfcf00.grib2'.format(\n",
    "                'gs://high-resolution-rapid-refresh',\n",
    "                dt.year, dt.month, dt.day, dt.hour)\n",
    "print(FILENAME)\n",
    "\n",
    "import xarray as xr\n",
    "import tensorflow as tf\n",
    "import tempfile\n",
    "import cfgrib\n",
    "import numpy as np\n",
    "\n",
    "refc = 0\n",
    "with tempfile.TemporaryDirectory() as tmpdirname:\n",
    "    TMPFILE=\"{}/read_grib\".format(tmpdirname)\n",
    "    tf.io.gfile.copy(FILENAME, TMPFILE, overwrite=True)\n",
    "    #ds = xr.open_dataset(TMPFILE, engine='cfgrib', backend_kwargs={'filter_by_keys': {'typeOfLevel': 'surface', 'stepType': 'instant'}})\n",
    "    #ds.data_vars['prate'].plot()  # crain, prate\n",
    "    ds = xr.open_dataset(TMPFILE, engine='cfgrib', backend_kwargs={'filter_by_keys': {'typeOfLevel': 'unknown', 'stepType': 'instant'}})\n",
    "    #ds = xr.open_dataset(TMPFILE, engine='cfgrib', backend_kwargs={'filter_by_keys': {'typeOfLevel': 'atmosphere', 'stepType': 'instant'}})\n",
    "    refc = ds.data_vars['refc']\n",
    "    refc.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Searching for similar images\n",
    "\n",
    "Suppose we want to find an image similar to the above image. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>sqdist</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019-09-20 05:00:00+00:00</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019-09-20 06:00:00+00:00</td>\n",
       "      <td>0.519979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019-09-20 04:00:00+00:00</td>\n",
       "      <td>0.546595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019-09-20 07:00:00+00:00</td>\n",
       "      <td>1.001852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019-09-20 03:00:00+00:00</td>\n",
       "      <td>1.387520</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       time    sqdist\n",
       "0 2019-09-20 05:00:00+00:00  0.000000\n",
       "1 2019-09-20 06:00:00+00:00  0.519979\n",
       "2 2019-09-20 04:00:00+00:00  0.546595\n",
       "3 2019-09-20 07:00:00+00:00  1.001852\n",
       "4 2019-09-20 03:00:00+00:00  1.387520"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%bigquery\n",
    "WITH ref1 AS (\n",
    "SELECT time AS ref1_time, ref1_value, ref1_offset\n",
    "FROM `ai-analytics-solutions.advdata.wxembed`,\n",
    "     UNNEST(ref) AS ref1_value WITH OFFSET AS ref1_offset\n",
    "WHERE time = '2019-09-20 05:00:00 UTC'\n",
    ")\n",
    "\n",
    "SELECT \n",
    "  time,\n",
    "  SUM( (ref1_value - ref[OFFSET(ref1_offset)]) * (ref1_value - ref[OFFSET(ref1_offset)]) ) AS sqdist \n",
    "FROM ref1, `ai-analytics-solutions.advdata.wxembed`\n",
    "GROUP BY 1\n",
    "ORDER By sqdist ASC\n",
    "LIMIT 5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This makes a lot of sense. The image from the previous/next hour is the most similar.\n",
    "Then, images from +/- 2 hours ...\n",
    "\n",
    "What if we want to find the most similar image that is not within +/- 1 day?\n",
    "Since we have only 1 year of data, we are not going to great analogs, possibly,\n",
    "but let's see what we get."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%bigquery df\n",
    "WITH ref1 AS (\n",
    "SELECT time AS ref1_time, ref1_value, ref1_offset\n",
    "FROM `ai-analytics-solutions.advdata.wxembed`,\n",
    "     UNNEST(ref) AS ref1_value WITH OFFSET AS ref1_offset\n",
    "WHERE time = '2019-09-20 05:00:00 UTC'\n",
    ")\n",
    "\n",
    "SELECT \n",
    "  time,\n",
    "  SUM( (ref1_value - ref[OFFSET(ref1_offset)]) * (ref1_value - ref[OFFSET(ref1_offset)]) ) AS sqdist \n",
    "FROM ref1, `ai-analytics-solutions.advdata.wxembed`\n",
    "WHERE time NOT BETWEEN '2019-09-19' AND '2019-09-21'\n",
    "GROUP BY 1\n",
    "ORDER By sqdist ASC\n",
    "LIMIT 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>sqdist</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019-01-02 00:00:00+00:00</td>\n",
       "      <td>5.125657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019-01-01 23:00:00+00:00</td>\n",
       "      <td>5.160568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019-07-01 18:00:00+00:00</td>\n",
       "      <td>5.226617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019-01-02 02:00:00+00:00</td>\n",
       "      <td>5.240267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019-07-01 19:00:00+00:00</td>\n",
       "      <td>5.244619</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       time    sqdist\n",
       "0 2019-01-02 00:00:00+00:00  5.125657\n",
       "1 2019-01-01 23:00:00+00:00  5.160568\n",
       "2 2019-07-01 18:00:00+00:00  5.226617\n",
       "3 2019-01-02 02:00:00+00:00  5.240267\n",
       "4 2019-07-01 19:00:00+00:00  5.244619"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Really, Jan. 2 in 2019 had similar weather to Sep 20? Let's see ..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gs://high-resolution-rapid-refresh/hrrr.20190102/conus/hrrr.t00z.wrfsfcf00.grib2\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "dt = pd.Timestamp(df['time'].values[0])\n",
    "# gs://high-resolution-rapid-refresh/hrrr.20200811/conus/hrrr.t04z.wrfsfcf00.grib2\n",
    "FILENAME = '{}/hrrr.{:4}{:02}{:02}/conus/hrrr.t{:02}z.wrfsfcf00.grib2'.format(\n",
    "                'gs://high-resolution-rapid-refresh',\n",
    "                dt.year, dt.month, dt.day, dt.hour)\n",
    "print(FILENAME)\n",
    "\n",
    "import xarray as xr\n",
    "import tensorflow as tf\n",
    "import tempfile\n",
    "import cfgrib\n",
    "import numpy as np\n",
    "\n",
    "refc = 0\n",
    "with tempfile.TemporaryDirectory() as tmpdirname:\n",
    "    TMPFILE=\"{}/read_grib\".format(tmpdirname)\n",
    "    tf.io.gfile.copy(FILENAME, TMPFILE, overwrite=True)\n",
    "    #ds = xr.open_dataset(TMPFILE, engine='cfgrib', backend_kwargs={'filter_by_keys': {'typeOfLevel': 'surface', 'stepType': 'instant'}})\n",
    "    #ds.data_vars['prate'].plot()  # crain, prate\n",
    "    ds = xr.open_dataset(TMPFILE, engine='cfgrib', backend_kwargs={'filter_by_keys': {'typeOfLevel': 'unknown', 'stepType': 'instant'}})\n",
    "    #ds = xr.open_dataset(TMPFILE, engine='cfgrib', backend_kwargs={'filter_by_keys': {'typeOfLevel': 'atmosphere', 'stepType': 'instant'}})\n",
    "    refc = ds.data_vars['refc']\n",
    "    refc.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What about July 1, 2019, which is next on the list?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gs://high-resolution-rapid-refresh/hrrr.20190701/conus/hrrr.t18z.wrfsfcf00.grib2\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "dt = pd.Timestamp(df['time'].values[2])\n",
    "# gs://high-resolution-rapid-refresh/hrrr.20200811/conus/hrrr.t04z.wrfsfcf00.grib2\n",
    "FILENAME = '{}/hrrr.{:4}{:02}{:02}/conus/hrrr.t{:02}z.wrfsfcf00.grib2'.format(\n",
    "                'gs://high-resolution-rapid-refresh',\n",
    "                dt.year, dt.month, dt.day, dt.hour)\n",
    "print(FILENAME)\n",
    "\n",
    "import xarray as xr\n",
    "import tensorflow as tf\n",
    "import tempfile\n",
    "import cfgrib\n",
    "import numpy as np\n",
    "\n",
    "refc = 0\n",
    "with tempfile.TemporaryDirectory() as tmpdirname:\n",
    "    TMPFILE=\"{}/read_grib\".format(tmpdirname)\n",
    "    tf.io.gfile.copy(FILENAME, TMPFILE, overwrite=True)\n",
    "    #ds = xr.open_dataset(TMPFILE, engine='cfgrib', backend_kwargs={'filter_by_keys': {'typeOfLevel': 'surface', 'stepType': 'instant'}})\n",
    "    #ds.data_vars['prate'].plot()  # crain, prate\n",
    "    ds = xr.open_dataset(TMPFILE, engine='cfgrib', backend_kwargs={'filter_by_keys': {'typeOfLevel': 'unknown', 'stepType': 'instant'}})\n",
    "    #ds = xr.open_dataset(TMPFILE, engine='cfgrib', backend_kwargs={'filter_by_keys': {'typeOfLevel': 'atmosphere', 'stepType': 'instant'}})\n",
    "    refc = ds.data_vars['refc']\n",
    "    refc.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Both these have weather in approximately the same places. On Jan 1, things are more widespread than in July.\n",
    "The date we searched for (May) is somewhere in between the Jan and July weather scenarios ...\n",
    "\n",
    "To make the search work better, we should use smaller tiles (not the whole country, but perhaps 500kmx500km tiles). Then, we'll get mesoscale phenomena."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Embeddings for interpolation ...\n",
    "\n",
    "Can we use the embeddings for interpolating between images?\n",
    "Recall that the sqdist between subsequent hours is about 0.5\n",
    "What happens if we use the image at t-1 and t+1 to get t=0?\n",
    "What's the error?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sqdist</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.105933</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     sqdist\n",
       "0  0.105933"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%bigquery\n",
    "WITH refl1 AS (\n",
    "SELECT ref1_value, idx\n",
    "FROM `ai-analytics-solutions.advdata.wxembed`,\n",
    "     UNNEST(ref) AS ref1_value WITH OFFSET AS idx\n",
    "WHERE time = '2019-09-20 05:00:00 UTC'\n",
    "),\n",
    "\n",
    "refl2 AS (\n",
    "SELECT ref2_value, idx\n",
    "FROM `ai-analytics-solutions.advdata.wxembed`,\n",
    "     UNNEST(ref) AS ref2_value WITH OFFSET AS idx\n",
    "WHERE time = '2019-09-20 06:00:00 UTC'\n",
    "),\n",
    "\n",
    "refl3 AS (\n",
    "SELECT ref3_value, idx\n",
    "FROM `ai-analytics-solutions.advdata.wxembed`,\n",
    "     UNNEST(ref) AS ref3_value WITH OFFSET AS idx\n",
    "WHERE time = '2019-09-20 07:00:00 UTC'\n",
    ")\n",
    "\n",
    "-- SELECT idx, ref1_value, ref2_value, ref3_value, ABS( ref2_value - (ref1_value + ref3_value)/2 ) AS diff\n",
    "SELECT SUM( (ref2_value - (ref1_value + ref3_value)/2) * (ref2_value - (ref1_value + ref3_value)/2) ) AS sqdist\n",
    "FROM refl1\n",
    "JOIN refl2 USING (idx)\n",
    "JOIN refl3 USING (idx)\n",
    "-- ORDER BY idx ASC"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Clustering the embeddings\n",
    "\n",
    "If the differences between images are meaningful, then it makes sense that we could cluster the images using just the embeddings.\n",
    "Let's do K-Means clustering into 5 categories and visualize the five centroids."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "CREATE TEMPORARY FUNCTION arr_to_input(arr ARRAY<FLOAT64>)\n",
      "RETURNS \n",
      "STRUCT<u1 FLOAT64, u2 FLOAT64, u3 FLOAT64, u4 FLOAT64, u5 FLOAT64, u6 FLOAT64, u7 FLOAT64, u8 FLOAT64, u9 FLOAT64, u10 FLOAT64, u11 FLOAT64, u12 FLOAT64, u13 FLOAT64, u14 FLOAT64, u15 FLOAT64, u16 FLOAT64, u17 FLOAT64, u18 FLOAT64, u19 FLOAT64, u20 FLOAT64, u21 FLOAT64, u22 FLOAT64, u23 FLOAT64, u24 FLOAT64, u25 FLOAT64, u26 FLOAT64, u27 FLOAT64, u28 FLOAT64, u29 FLOAT64, u30 FLOAT64, u31 FLOAT64, u32 FLOAT64, u33 FLOAT64, u34 FLOAT64, u35 FLOAT64, u36 FLOAT64, u37 FLOAT64, u38 FLOAT64, u39 FLOAT64, u40 FLOAT64, u41 FLOAT64, u42 FLOAT64, u43 FLOAT64, u44 FLOAT64, u45 FLOAT64, u46 FLOAT64, u47 FLOAT64, u48 FLOAT64, u49 FLOAT64, u50 FLOAT64>\n",
      "AS (STRUCT(\n",
      "   arr[OFFSET(0)], arr[OFFSET(1)], arr[OFFSET(2)], arr[OFFSET(3)], arr[OFFSET(4)], arr[OFFSET(5)], arr[OFFSET(6)], arr[OFFSET(7)], arr[OFFSET(8)], arr[OFFSET(9)], arr[OFFSET(10)], arr[OFFSET(11)], arr[OFFSET(12)], arr[OFFSET(13)], arr[OFFSET(14)], arr[OFFSET(15)], arr[OFFSET(16)], arr[OFFSET(17)], arr[OFFSET(18)], arr[OFFSET(19)], arr[OFFSET(20)], arr[OFFSET(21)], arr[OFFSET(22)], arr[OFFSET(23)], arr[OFFSET(24)], arr[OFFSET(25)], arr[OFFSET(26)], arr[OFFSET(27)], arr[OFFSET(28)], arr[OFFSET(29)], arr[OFFSET(30)], arr[OFFSET(31)], arr[OFFSET(32)], arr[OFFSET(33)], arr[OFFSET(34)], arr[OFFSET(35)], arr[OFFSET(36)], arr[OFFSET(37)], arr[OFFSET(38)], arr[OFFSET(39)], arr[OFFSET(40)], arr[OFFSET(41)], arr[OFFSET(42)], arr[OFFSET(43)], arr[OFFSET(44)], arr[OFFSET(45)], arr[OFFSET(46)], arr[OFFSET(47)], arr[OFFSET(48)], arr[OFFSET(49)]\n",
      "));\n"
     ]
    }
   ],
   "source": [
    "# Unfortunately, BigQueryML does not accept arrays as input\n",
    "# so we convert it into a struct. Generate the boilerplate code ...\n",
    "def create_array_to_struct(N):\n",
    "    sql = (\"\"\"\n",
    "CREATE TEMPORARY FUNCTION arr_to_input(arr ARRAY<FLOAT64>)\n",
    "RETURNS \n",
    "STRUCT<\"\"\" \n",
    "    + ', '.join([\"u{} FLOAT64\".format(idx+1) for idx in range(N)]) + \">\\n\"\n",
    "    + \"AS (STRUCT(\\n\"\n",
    "    + ', '.join([\"arr[OFFSET({})]\".format(idx) for idx in range(N)])\n",
    "    + \"\\n));\"\n",
    "    )\n",
    "    return sql\n",
    "    \n",
    "print(create_array_to_struct(50))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: []\n",
       "Index: []"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%bigquery\n",
    "\n",
    "-- Unfortunately, BigQueryML does not accept arrays as input, so we convert it into a struct\n",
    "\n",
    "CREATE TEMPORARY FUNCTION arr_to_input(arr ARRAY<FLOAT64>)\n",
    "RETURNS \n",
    "STRUCT<u1 FLOAT64, u2 FLOAT64, u3 FLOAT64, u4 FLOAT64, u5 FLOAT64,\n",
    "u6 FLOAT64, u7 FLOAT64, u8 FLOAT64, u9 FLOAT64, u10 FLOAT64, \n",
    "u11 FLOAT64, u12 FLOAT64, u13 FLOAT64, u14 FLOAT64, u15 FLOAT64,\n",
    "u16 FLOAT64, u17 FLOAT64, u18 FLOAT64, u19 FLOAT64, u20 FLOAT64,\n",
    "\n",
    "u21 FLOAT64, u22 FLOAT64, u23 FLOAT64, u24 FLOAT64, u25 FLOAT64,\n",
    "u26 FLOAT64, u27 FLOAT64, u28 FLOAT64, u29 FLOAT64, u30 FLOAT64,\n",
    "\n",
    "u31 FLOAT64, u32 FLOAT64, u33 FLOAT64, u34 FLOAT64, u35 FLOAT64,\n",
    "u36 FLOAT64, u37 FLOAT64, u38 FLOAT64, u39 FLOAT64, u40 FLOAT64,\n",
    "\n",
    "u41 FLOAT64, u42 FLOAT64, u43 FLOAT64, u44 FLOAT64, u45 FLOAT64,\n",
    "u46 FLOAT64, u47 FLOAT64, u48 FLOAT64, u49 FLOAT64, u50 FLOAT64>\n",
    "\n",
    "AS (\n",
    "STRUCT(\n",
    "    arr[OFFSET(0)], arr[OFFSET(1)], arr[OFFSET(2)], arr[OFFSET(3)], arr[OFFSET(4)]\n",
    "    , arr[OFFSET(5)], arr[OFFSET(6)], arr[OFFSET(7)], arr[OFFSET(8)], arr[OFFSET(9)]\n",
    "    , arr[OFFSET(10)], arr[OFFSET(11)], arr[OFFSET(12)], arr[OFFSET(13)], arr[OFFSET(14)]\n",
    "    , arr[OFFSET(15)], arr[OFFSET(16)], arr[OFFSET(17)], arr[OFFSET(18)], arr[OFFSET(19)]\n",
    "    , arr[OFFSET(20)], arr[OFFSET(21)], arr[OFFSET(22)], arr[OFFSET(23)], arr[OFFSET(24)]\n",
    "    , arr[OFFSET(25)], arr[OFFSET(26)], arr[OFFSET(27)], arr[OFFSET(28)], arr[OFFSET(29)]\n",
    "    , arr[OFFSET(30)], arr[OFFSET(31)], arr[OFFSET(32)], arr[OFFSET(33)], arr[OFFSET(34)]\n",
    "    , arr[OFFSET(35)], arr[OFFSET(36)], arr[OFFSET(37)], arr[OFFSET(38)], arr[OFFSET(39)]\n",
    "    , arr[OFFSET(40)], arr[OFFSET(41)], arr[OFFSET(42)], arr[OFFSET(43)], arr[OFFSET(44)]\n",
    "    , arr[OFFSET(45)], arr[OFFSET(46)], arr[OFFSET(47)], arr[OFFSET(48)], arr[OFFSET(49)]\n",
    "));\n",
    "\n",
    "\n",
    "CREATE OR REPLACE MODEL advdata.hrrr_clusters\n",
    "OPTIONS(model_type='kmeans', num_clusters=5, KMEANS_INIT_METHOD='KMEANS++')\n",
    "AS\n",
    "\n",
    "SELECT arr_to_input(ref) AS ref\n",
    "FROM `ai-analytics-solutions.advdata.wxembed`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>centroid_id</th>\n",
       "      <th>feature</th>\n",
       "      <th>numerical_value</th>\n",
       "      <th>categorical_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>ref_u3</td>\n",
       "      <td>0.053409</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>ref_u5</td>\n",
       "      <td>0.193218</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>ref_u4</td>\n",
       "      <td>-0.029938</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>ref_u2</td>\n",
       "      <td>0.021334</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>ref_u1</td>\n",
       "      <td>0.094913</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   centroid_id feature  numerical_value categorical_value\n",
       "0            1  ref_u3         0.053409                []\n",
       "1            1  ref_u5         0.193218                []\n",
       "2            1  ref_u4        -0.029938                []\n",
       "3            1  ref_u2         0.021334                []\n",
       "4            1  ref_u1         0.094913                []"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%bigquery\n",
    "SELECT * FROM ML.CENTROIDS(MODEL advdata.hrrr_clusters) LIMIT 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%bigquery df\n",
    "SELECT centroid_id, CAST(REPLACE(feature, 'ref_u', '') AS INT64) AS feature, numerical_value \n",
    "FROM ML.CENTROIDS(MODEL advdata.hrrr_clusters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>centroid_id</th>\n",
       "      <th>feature</th>\n",
       "      <th>numerical_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.094913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.021334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0.053409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>-0.029938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>0.193218</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   centroid_id  feature  numerical_value\n",
       "0            1        1         0.094913\n",
       "1            1        2         0.021334\n",
       "2            1        3         0.053409\n",
       "3            1        4        -0.029938\n",
       "4            1        5         0.193218"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.0949131 ,  0.02133422,  0.0534094 , -0.02993772,  0.19321818,\n",
       "        0.01875934,  0.06888813,  0.00805591, -0.021081  , -0.02578681,\n",
       "       -0.00528643,  0.14018791,  0.05266802,  0.08445446, -0.07094356,\n",
       "        0.02388524,  0.14820323, -0.02565688, -0.19578159, -0.01971966,\n",
       "       -0.03471923, -0.14031188, -0.10975839,  0.05519118,  0.00988916,\n",
       "        0.02997833, -0.18832657, -0.00707636,  0.13384155,  0.13228441,\n",
       "        0.0082098 ,  0.13009384,  0.07743392, -0.02554795, -0.00296324,\n",
       "        0.11755495,  0.08520417, -0.00185651,  0.11564447, -0.13924349,\n",
       "        0.17615114, -0.25502104,  0.15088167, -0.12591782,  0.02078161,\n",
       "        0.20640734,  0.00729936, -0.13711191, -0.09992737,  0.08051693])"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['centroid_id']==1].sort_values(by='feature')['numerical_value'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"decoder\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "embed_input (InputLayer)     [(None, 50)]              0         \n",
      "_________________________________________________________________\n",
      "decoder_dense (Dense)        (None, 3584)              182784    \n",
      "_________________________________________________________________\n",
      "decoder_reshape (Reshape)    (None, 4, 7, 128)         0         \n",
      "_________________________________________________________________\n",
      "decoder_conv_0 (Conv2D)      (None, 4, 7, 128)         262272    \n",
      "_________________________________________________________________\n",
      "decoder_upsamp_0 (UpSampling (None, 16, 28, 128)       0         \n",
      "_________________________________________________________________\n",
      "decoder_conv_1 (Conv2D)      (None, 16, 28, 64)        131136    \n",
      "_________________________________________________________________\n",
      "decoder_upsamp_1 (UpSampling (None, 64, 112, 64)       0         \n",
      "_________________________________________________________________\n",
      "decoder_conv_2 (Conv2D)      (None, 64, 112, 32)       32800     \n",
      "_________________________________________________________________\n",
      "decoder_upsamp_2 (UpSampling (None, 256, 448, 32)      0         \n",
      "_________________________________________________________________\n",
      "decoder_conv_3 (Conv2D)      (None, 256, 448, 16)      8208      \n",
      "_________________________________________________________________\n",
      "decoder_upsamp_3 (UpSampling (None, 1024, 1792, 16)    0         \n",
      "_________________________________________________________________\n",
      "before_padding (Conv2D)      (None, 1024, 1792, 1)     145       \n",
      "_________________________________________________________________\n",
      "refc_reconstructed (ZeroPadd (None, 1059, 1799, 1)     0         \n",
      "=================================================================\n",
      "Total params: 617,345\n",
      "Trainable params: 617,345\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "def create_decoder(model_dir):\n",
    "    model = tf.keras.models.load_model(model_dir)\n",
    "    decoder_input = tf.keras.Input([50], name='embed_input')\n",
    "    embed_seen = False\n",
    "    x = decoder_input\n",
    "    for layer in model.layers:\n",
    "        if embed_seen:\n",
    "            x = layer(x)\n",
    "        elif layer.name == 'refc_embedding':\n",
    "            embed_seen = True\n",
    "    decoder = tf.keras.Model(decoder_input, x, name='decoder')\n",
    "    print(decoder.summary())\n",
    "    return decoder\n",
    "\n",
    "decoder = create_decoder('gs://ai-analytics-solutions-kfpdemo/wxsearch/trained/savedmodel')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12.15992\n",
      "18.481905\n",
      "11.578112\n",
      "20.12916\n",
      "18.126585\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "f, axarr = plt.subplots(3,2, figsize=(15,15))\n",
    "for cid in range(1,6):\n",
    "    embed = df[df['centroid_id']==cid].sort_values(by='feature')['numerical_value'].values\n",
    "    embed = tf.reshape( tf.convert_to_tensor(embed, dtype=tf.float32), [-1, 50])\n",
    "    outimg = decoder.predict(embed).squeeze() * 60\n",
    "    print(np.max(outimg))\n",
    "    axarr[ (cid-1)//2, (cid-1)%2].imshow(outimg, origin='lower');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Enjoy!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Copyright 2020 Google Inc. Licensed under the Apache License, Version 2.0 (the \"License\"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License"
   ]
  }
 ],
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